AU2020323825B2

AU2020323825B2 - Kit for predicting or diagnosing nonalcoholic fatty liver disease, and method for diagnosing nonalcoholic fatty liver disease

Info

Publication number: AU2020323825B2
Application number: AU2020323825A
Authority: AU
Inventors: Won Kim; Gwang Pyo Ko; Giljae Lee; Hyun Ju You
Original assignee: Kobiolabs Inc
Current assignee: Kobiolabs Inc
Priority date: 2019-07-30
Filing date: 2020-07-30
Publication date: 2024-05-09
Anticipated expiration: 2040-07-30
Also published as: WO2021020923A1; MX2022000801A; JP2022542944A; CA3148431A1; WO2021020920A2; JP7303372B2; US20220257669A1; AU2020323825A1; BR112022001637A2; US20220276246A1; CA3148434A1; JP2022542506A; WO2021020920A3; BR112022001602A2; MX2022000802A; JP2024056699A; JP7499841B2; AU2020320244A1

Abstract

The present invention relates, with respect to nonalcoholic fatty liver disease, to a kit for predicting or diagnosing the risk of the disease, a method for providing information for predicting or diagnosing the risk of the disease, a method for screening a therapeutic agent for the disease, and a pharmaceutical composition for prevention or treatment of the disease. Specifically, the present invention can effectively predict or diagnose the risk of nonalcoholic fatty liver disease and, in particular, has superior significance in prediction and information provision for non-obese subjects. Accordingly, the present invention can be effectively used in the prevention or treatment of the disease by providing effective information on nonalcoholic fatty liver disease. In addition, the pharmaceutical composition for prevention or treatment can be effectively used in the treatment of nonalcoholic fatty liver disease.

Description

[DESCRIPTION] [TITLE OF THE INVENTION] KIT FOR PREDICTING OR DIAGNOSING NONALCOHOLIC FATTY LIVER DISEASE, AND METHOD FOR DIAGNOSING NONALCOHOLIC FATTY LIVER DISEASE [TECHNICAL FIELD]

The present invention relates to a kit for predicting or diagnosing nonalcoholic

fatty liver disease, and a method for diagnosing thereof.

[BACKGROUND ART]

Nonalcoholic fatty liver disease (NAFLD) is characterized by liver disease of

metabolic disorders ranging from simple steatosis, to nonalcoholic steatohepatitis

(NASH) which is an aggressive tissue form that ultimately leads to advanced fibrosis

and cirrhosis. The global prevalence of NAFLD is estimated to be 24-30% in most

epidemiological studies, and is increasing in parallel with obesity and metabolic

syndrome. Although NAFLD is commonly associated with obesity, clinical symptoms

and pathological severity similar to those observed in obese NAFLD patients may occur

in non-obese subjects. Without considering the cut-off of the different body mass index

(BMI) defining obesity (Asian >25, other races >30), it is consistently reported that 3-30%

of the non-obese population has NAFLD in both the West and the East. Although

visceral fat, food composition and genetic factors may be associated with non-obese

NAFLD, additional studies considering other environmental factors are needed to

elucidate the pathogenesis of non-obese NAFLD.

Recently, increased interests have focused on identifying and understanding

specific roles of the gut microbiota in various metabolic diseases. Gut dysbiosis, which

refers to abnormal changes in the gut microbiota compared to the normal microbiota, is

associated with a decrease in bacteria producing beneficial short chain fatty acid

(SCFA), changes in bile acid composition, activation of immune response against

lipopolysaccharide (LPS), an increase of ethanol production by hyperplasia of ethanol

producing bacteria, and conversion of phosphatidylcholine into choline and

trimethylamine. Changes in the gut microbiome that affects the gut-liver axis contribute

to the progression of chronic liver disease such as NAFLD and cirrhosis and advanced

fibrosis.

Boursier et al. compared microbiome changes between patients with mild and

severs fibrosis, and observed significant intestinal bacterial imbalance and functional

changes in patients with severe fibrosis (Non-patent Document 1). Loomba et al. used

metagenomic data to identify 37 bacteria that were significantly enriched or

significantly reduced in NAFLD patients with advanced fibrosis, and proposed a

microbiome-based biomarker to predict fibrosis (Non-patent Document 2). Bajaj et al.

defined the gut microbiome profile during the progression of cirrhosis (Non-patent

Document 3). A Chinese cohort study observed changes in the gut microbiome of

cirrhosis patients (Non-patent Document 4). However, the microbial taxa associated

with disease severity and fibrosis stage were not consistent with previous NAFLD studies. This discrepancy may be due to the influence of regional differences

(Non-patent Document 5). However, differences in basic BMI status may explain these

inconsistent results. Moreover, specific changes in gut microbiome and related

metabolites in the non-obese NAFLD group were rarely defined.

Therefore, there is a need for a method for preventing, treating and diagnosing

non-obese NAFLD, which determines the histological severity of NAFLD,

well-characterizes the gut microbiome changes, and is effective.

[DISCLOSURE] [TECHNICAL PROBLEM]

The present invention is to solve the above problem, and its purpose is to

provide a detection marker of nonalcoholic fatty liver disease comprising one or more

of detection markers selected from the group consisting of a microbial biomarker, bile

acid and components thereof, and intestinal short chain fatty acid, a kit for predicting or

diagnosing a degree of risk of nonalcoholic fatty liver disease using the detection

marker, a method for predicting or diagnosing, or a method for providing information

for predicting or diagnosing the degree of risk of nonalcoholic fatty liver disease using

the detection marker, and a method for screening a therapeutic agent of nonalcoholic

fatty liver disease.

[TECHNICAL SOLUTION]

In order to achieve the above purpose, the present invention provides a

detection marker of nonalcoholic fatty liver disease and a kit for predicting or diagnosing a degree of risk of nonalcoholic fatty liver disease comprising one or more detection means detecting the detection marker.

The kit may be used for predicting or diagnosing a degree of risk of

nonalcoholic fatty liver disease by comprising the aforementioned means of detecting a

specific subject and specifying the amount, activity, population, and the like of the

specific detection subject, or comparing results with other detection subject.

In the present invention, diagnosis comprises confirming the presence or

absence of disease, degree of risk of disease, state of disease and prognosis of disease,

and comprises all types of analysis used to derive disease state and decision.

In an embodiment of the present invention, the nonalcoholic fatty liver disease

may be nonalcoholic fatty liver, nonalcoholic steatohepatitis or cirrhosis.

In an embodiment of the present invention, predicting the degree of risk of the

disease may predict the severity of fibrosis. The severity offibrosis may include F=O to

F=4, and F=O means no liver fibrosis; F=1 means mild liver fibrosis; F=2 means

significant liver fibrosis; F=3 means advanced liver fibrosis; and F=4 means cirrhosis.

In an embodiment of the present invention, the kit may be for a non-obese

patient, for example, a non-obese patient with BMI of 25 kg/m2 or less.

The detection marker of nonalcoholic fatty liver disease may comprise one or

more kinds among microbial biomarkers, total bile acid and components, and intestinal

short chain fatty acid.

In an embodiment of the present invention, the kit may comprise a detection

means capable of detecting one or more of detection markers selected from the following:

(a) one or more detection means respectively detecting one or more selected

from the group consisting of microbial biomarkers of nonalcoholic fatty liver disease;

(b) one or more detection means respectively detecting one or more selected

from the group consisting of total bile acid and components of bile acid; and

(c) one or more detection means respectively detecting one or more selected

from the group consisting of intestinal short chain fatty acids.

The kit may comprise for example, (a); (b); (c); (a) and (b); (a) and (c); (b) and

(c); or (a), (b), and (c).

In the present specification, the total bile acid and components of bile acid, the

short chain fatty acid, and the like are used as a meaning comprising all metabolites

thereof

The microbial biomarkers of nonalcoholic fatty liver disease, at the Family

level, may be one or more selected from the group consisting of Enterobacteriaceae,

Veillonellaceae, Rikenellaceae, Fusobacteriaceae, Ruminococcaceae, Lachnospiraceae,

Actinomycetaceae, Desulfovibrioceae, and Desulfovibrionaceaeat. For example, it may

be one or more selected from the group consisting of Enterobacteriaceae,

Veillonellaceae, Ruminococcaceae, Lachnospiraceae and Actinomycetaceae, one or

more selected from the group consisting of Enterobacteriaceae, Veillonellaceae,

Lachnospiraceae and Ruminococcaceae, or one or more selected from the group

consisting of Enterobacteriaceae, Veillonellaceae, and Ruminococcaceae.

One example of the Enterobacteriaceae microorganism may be one or more of

Citrobacter and Klebsiella, one example of the Veillonellaceae microorganism may be

one or more of Veillonella and Megamonas, one example of the Fusobacteriaceae

microorganism may be Fusobacterium, one example of the Desulfovibrionaceae

microorganism may be Desulfovibrio, the Ruminococcaceae microorganism may be one

or more of Ruminococcus, Faecalibacterium and Oscillospira, the Lachnospiraceae

microorganism may be one or more of Coprococcus and Lachnospira, and the

Actinomycetaceae microorganism may be actinomyces.

As one example, the (a) may be one or more of detection means each capable of

detecting one or more selected from the group consisting of Enterobacteriaceae,

Veillonellaceae, Lachnospiraceae and Ruminococcaceae.

The microbial biomarker according to the present invention, at the genus level,

may be one or more kinds selected from the group consisting of Citrobacter, Klebsiella,

Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus, and Lachnospira, and

specifically, it may be one or more kinds selected from the group consisting of

Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus, Veillonella, Megamonas,

and Lachnospira, and more specifically, it may be one or more kinds selected from the

group consisting of Ruminococcus, Faecalibacterium, Oscillospira, Coprococcus, and

Lachnospira, or one or more kinds selected from the group consisting of Veillonella and

Megamona.

The total bile acid and components of bile acid may be one or more selected

from the group consisting of primary bile acid comprising total bile acid, cholic acid

and chenodeoxycholic acid; and secondary bile acid comprising ursodeoxycholic acid, lithocholic acid and deoxycholic acid.

As one example, the (b) may be one or more of detection means each capable of

detecting one or more selected from the group consisting of total bile acid, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid and deoxycholic acid.

As one example, the (c) may be one or more of detection means each capable of

detecting one or more selected from the group consisting of acetate, propionate and

butyrate.

In an embodiment of the present invention, the kit may comprise one or more

selected from combinations of (a) to (h) as follows:

detection means of the (a),

detection means of the (b),

detection means of the (c),

(d) one or more detection means each capable of detecting one or more selected

from the group consisting of Enterobacteriaceae, Veillonellaceae, Lachnospiraceae,

Ruminococcaceae, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and

propionate;

(e) one or more detection means each capable of detecting one or more selected

from the group consisting of Ruminococcus, Faecalibacterium, Oscillospira,

Coprococcus, Lachnospira, total bile acid, cholic acid, chenodeoxycholic acid,

ursodeoxycholic acid, lithocholic acid and deoxycholic acid;

(f) one or more detection means each capable of detecting one or more selected

from the group consisting of Ruminococcus, Faecalibacterium, Oscillospira,

Coprococcus, Lachnospira and fecal propionate;

(g) one or more detection means each capable of detecting one or more selected

from the group consisting of Veillonella, Megamonas, total bile acid, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid and deoxycholic acid;

and

(h) one or more detection means each capable of detecting one or more selected

from the group consisting of Veillonella, Megamonas and fecal propionate.

The detection means of the (a) may be, for example, one or more detection

means each capable of detecting one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceae, Lachnospiraceae and Ruminococcaceae.

The detection means of the (b) may be, for example, one or more detection

means each capable of detecting one or more selected from the group consisting of total

bile acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid and

deoxycholic acid.

The detection means of the (c) may be, for example, one or more detection

means each capable of detecting one or more selected from the group consisting of short

chain fatty acid, acetate, propionate and butyrate.

In an embodiment of the present invention, the kit may comprise one or more

selected from combinations of (i) to (k) as follows:

(i) one or more detection means each capable of detecting one or more selected

from the group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,

Citrobacter, Klebsiella, Veillonella, Megamonas, Ruminococcus, Faecalibacterium and

Oscillospira;

() one or more detection means each capable of detecting one or more selected

from the group consisting of cholic acid, chenodeoxycholic acid, ursodeoxycholic acid

and metabolites thereof; and

(k) one or more detection means each capable of detecting one or more selected

from the group consisting of intestinal short chain fatty acids and propionate.

As one example, the detection marker of nonalcoholic fatty liver disease

according to the present invention may comprise one or more kinds selected from the

group consisting of Enterobacteriaceae, Veillonellaceae, Rikenellaceae,

Fusobacteriaceae, Ruminococcaceae, Lachnospiraceae, Actinomycetaceae,

Desulfovibrioceae, Desulfovibrionaceae, Citrobacter, Klebsiella, Veillonella,

Megamonas, Fusobacterium, Ruminococcus, Faecalibacterium, Oscillospira,

Coprococcus, Lachnospira, Actinomyces, Desulfovibrio, total bile acid, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid, lithocholic acid, deoxycholic acid, short

chain fatty acid, acetate, propionate and butyrate.

As one specific example, the detection marker of nonalcoholic fatty liver

disease according to the present invention may comprise Enterobacteriaceae,

Veillonellaceae, and Ruminococcaceae. According to the Examples of the present

application, in case of using the three bacterial markers in combination, non-alcoholic

fatty liver can be predicted with high accuracy of AUROC 0.8 or higher in a non-obese

subject (FIG. 5a).

As one specific example, the detection marker of nonalcoholic fatty liver disease according to the present invention may comprise Megamonas and

Ruminococcus. According to the Examples of the present application, in case of using

the two bacterial markers in combination, non-alcoholic fatty liver could be predicted

with high accuracy of AUROC 0.7 or higher in a non-obese subject (FIG. 5b).

As one specific example, the detection marker of nonalcoholic fatty liver

disease according to the present invention may comprise cholic acid, chenodeoxycholic

acid, ursodeoxycholic acid, and propionate. According to the Examples of the present

application, in case of using 4 metabolite markers in combination, non-alcoholic fatty

liver could be predicted with high accuracy of AUROC 0.7 or higher in a non-obese

subject.

As one specific example, the detection marker of nonalcoholic fatty liver

disease according to the present invention may comprise Enterobacteriaceae,

Veillonellaceae, Ruminococcaceae, cholic acid, chenodeoxycholic acid,

ursodeoxycholic acid, and propionate. According to the Examples of the present

application, in case of using the 3 bacterial markers and 4 metabolite markers in

combination, non-alcoholic fatty liver could be predicted with high accuracy of

AUROC 0.9 or higher in a non-obese subject. Otherwise, the detection marker of

nonalcoholic fatty liver disease according to the present invention may comprise

Megamonas, Ruminococcus, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid,

and propionate. According to the Examples of the present application, in case of using

the 2 bacterial markers and 4 metabolite markers in combination, non-alcoholic fatty

liver could be predicted with high accuracy of AUROC 0.9 or higher in a non-obese subject. This was significantly higher accuracy than the biomarker of non-alcoholic fatty liver used conventionally, and thus, it could be seen that the biomarker for predicting non-alcoholic fatty liver according to the present invention could predict non-alcoholic fatty liver accurately, and in particular, it could predict non-alcoholic fatty liver of a non-obese subject more accurately.

The present invention provides a method for predicting or diagnosing, or a

method for providing information for predicting or diagnosing a degree of risk of

nonalcoholic fatty liver disease comprising detecting one or more of detection markers

selected from the group consisting of (a) one or more detection markers selected from

the group consisting of microbial biomarkers of nonalcoholic fatty liver disease; (b) one

or more detection markers selected from the group consisting of total bile acid and

components of bile acid; and (c) one or more detection markers selected from the group

consisting of intestinal short chain fatty acids.

In an embodiment of the present invention, the nonalcoholic fatty liver disease

may be nonalcoholic fatty liver, nonalcoholic steatohepatitis or cirrhosis. In an

embodiment of the present invention, predicting the degree of risk may be predicting the

severity of fibrosis. In an embodiment of the present invention, the method for

diagnosing or method for providing information for diagnosing may be for a non-obese

patient with BMI<25 kg/m2.

In an embodiment of the present invention, the method may comprise one or

more steps selected from the following:

(i) measuring abundance of microbial biomarkers as one or more detection markers selected from the group consisting of microbial biomarkers of nonalcoholic fatty liver disease;

(ii) measuring the content in feces of one or more detection markers selected

from the group consisting of total bile acid and components of bile acid; and

(iii) measuring the content in feces of one or more detection markers selected

from the group consisting of intestinal short chain fatty acid, for example, acetate,

propionate and butyrate.

In an embodiment of the present invention, the method may comprise

comparing the detected values of a subject individual, with a reference value of a

healthy individual corresponding thereto, for detection markers (a) to (c), (a) to (h), (i)

to (k), or (a) to (k) which can be comprised in the kit.

In an embodiment of the present invention, the method may comprise

determining that the severity of fibrosis is high, the detected value of the subject

individual compared to the reference value of the healthy individual is increased or

decreased according to an increase or decrease of the following detection markers, as

the result of comparing the detected values of the subject and reference value of the

healthy individual corresponding thereto:

(a) with respect to one or more detection markers selected from the group

consisting of microbial biomarkers of nonalcoholic fatty liver disease, the abundance of

Enterobacteriaceae is increased, the abundance of Veillonellaceae is increased, or the

abundance of Ruminococcaceae is decreased,

(b) with respect to one or more detection markers selected from the group consisting of total bile acid and components of bile acid, the content of cholic acid in feces is increased, the content of chenodeoxycholic acid in feces is increased, or the content ofursodeoxycholic acid in feces is increased, or

(c) with respect to one or more detection markers selected from the group

consisting of intestinal short chain fatty acids, for example, in case that the content of

one or more selected from the group consisting of acetate, propionate and butyrate in

feces is increased, as one example, in case that the content of propionate in feces is

increased, it may comprise determining that the severity of fibrosis is high.

The present invention provides a method for screening a therapeutic agent for

nonalcoholic fatty liver disease comprising the following steps:

(1) administering a test substance to an experimental animal having

nonalcoholic fatty liver disease;

(2) measuring one or more detection markers selected from the group consisting

of (a) one or more detection markers selected from the group consisting of microbial

biomarkers of nonalcoholic fatty liver disease; (b) one or more detection markers

selected from the group consisting of total bile acid and components of bile acid; and (c)

one or more detection markers selected from the group consisting of intestinal short

chain fatty acids, in the experimental animal untreated with the test substance and the

experimental animal treated with the test substance; and

(3) comparing the measured results in a control group untreated with the test

substance and an experimental group administered with the test substance.

In an embodiment of the present invention, the nonalcoholic fatty liver disease may be nonalcoholic fatty liver, nonalcoholic steatohepatitis or cirrhosis.

In an embodiment of the present invention, the experimental animal of the step

(1) may have one or more characteristics of the following (1) to (5):

(1) A condition in which the blood ALT concentration is increased, for example,

a condition in which it is over 1 time, 1.1 times or more, 1.2 times or more, 1.3 times or

more, 1.4 times or more, 1.5 times or more, 1.6 times or more, 1.7 times or more, 1.8

times or more, 1.9 times or more, 2 times or more, 2.1 times or more, 2.2 times or more,

2.3 times or more, 2.4 times or more, 2.5 times or more, 2.6 times or more, 2.7 times or

more, 2.8 times or more, 2.9 times or more, 3 times or more, 3.5 times or more, 4 times

or more, 4.5 times or more, 5 times or more, 5.5 times or more, 6 times or more, 6.5

times or more, 7 times or more, 7.5 times or more, 8 times or more, 8.5 times or more, 9

times or more, 9.5 times or more, or 10 times or more, of the blood ALT concentration

of a normal control group.

(2) A condition in which the blood AST concentration is increased, for example,

times or more, 9.5 times or more, or 10 times or more, of the blood AST concentration of a normal control group.

(3) A condition in which the secondary bile acid concentration in cecum is

decreased, for example, a condition in which it is less than 1 time, 0.9 times or less, 0.8

times or less, 0.7 times or less, 0.6 times or less, 0.5 times or less, 0.4 times or less, 0.3

times or less, 0.2 times or less, or 0.1 time or less, of the secondary bile acid

concentration in cecum of a normal control group.

(4) A condition in which the fibrosis marker gene expression is increased, for

example, a condition in which it is overexpressed more than 1 time, 1.1 times or more,

1.2 times or more, 1.3 times or more, 1.4 times or more, 1.5 times or more, 1.6 times or

more, 1.7 times or more, 1.8 times or more, 1.9 times or more, 2 times or more, 2.1

times or more, 2.2 times or more, 2.3 times or more, 2.4 times or more, 2.5 times or

more, 2.6 times or more, 2.7 times or more, 2.8 times or more, 2.9 times or more, 3

times or more, 3.5 times or more, 4 times or more, 4.5 times or more, 5 times or more,

5.5 times or more, 6 times or more, 6.5 times or more, 7 times or more, 7.5 times or

more, 8 times or more, 8.5 times or more, 9 times or more, 9.5 times or more, 10 times

or more, 11 times or more, 12 times or more, 13 times or more, 14 times or more, 15

times or more, 16 times or more, 17 times or more, 18 times or more, 19 times or more,

or 20 times or more, of the fibrosis marker gene expression of a normal control group.

The fibrosis marker gene may be one or more kinds selected from the group consisting

of Collal, TimpI, and a-SMA.

(5) A condition in which the liver weight ratio to body weight is increased, for

example, a condition in which it is over 1 time, 1.1 times or more, 1.2 times or more,

1.3 times or more, 1.4 times or more, 1.5 times or more, 1.6 times or more, 1.7 times or

more, 1.8 times or more, 1.9 times or more, 2 times or more, 2.1 times or more, 2.2

times or more, 2.3 times or more, 2.4 times or more, 2.5 times or more, 2.6 times or

more, 2.7 times or more, 2.8 times or more, 2.9 times or more, or 3 times or more, of the

liver weight ratio to body weight of a normal control group.

In an embodiment of the present invention, the test substance may comprise a

candidate substance of a therapeutic agent for nonalcoholic fatty liver disease, and for

example, it may be one or more selected from the group consisting of peptide, protein,

nonpeptide compound, active compound, fermented product, cell extract, plant extract,

animal tissue extract and plasma.

In an embodiment of the present invention, the method may comprise one or

more steps selected in the following:

measuring the content in feces of one or more detection markers selected from

the group consisting of

(i) measuring abundance of microbial biomarkers as one or more detection

markers selected from the group consisting of microbial biomarkers of nonalcoholic

fatty liver disease;

(ii) measuring the content in feces of one or more detection markers selected

from the group consisting of total bile acid and components of bile acid; and

(iii) measuring the content in feces of one or more detection markers selected

propionate and butyrate.

In an embodiment of the present invention, the method may comprise

comparing detection values of an experimental group administered with a test substance

and a control group not administered with the test substance for an experimental animal

having nonalcoholic fatty liver disease, for detection markers (a) to (c), (a) to (h), (i) to

(k), or (a) to (k).

In an embodiment of the present invention, the method may comprise selecting

a test substance as a therapeutic agent of nonalcoholic fatty liver disease, according to

an increase or decrease result for each detection marker of the detected value of the

experimental group compared to a reference value of the control group, as the result of

comparing the detected values of an experimental group and the detected value of a

control group:

(a) with respect to one or more detection markers selected from the group

abundance of Ruminococcaceae is decreased,

(b) with respect to one or more detection markers selected from the group

consisting of total bile acid and components of bile acid, the content in feces of cholic

acid is increased, the content in feces of chenodeoxycholic acid is increased, or the

content in feces ofursodeoxycholic acid is increased, or

(c) with respect to one or more detection markers selected from the group

consisting of intestinal short chain fatty acids, for example, in case that the content in

feces of one or more selected from the group consisting of acetate, propionate and butyrate is increased, or as one example, the content in feces of propionate is increased, a step of determining that the severity of fibrosis is high may be comprised.

In an embodiment of the present invention, the method may comprise selecting

a test substance in which the abundance of Enterobacteriaceae is decreased, the

abundance of Veillonellaceae is decreased, the abundance of Ruminococcaceae is

increased, the content in feces of cholic acid is decreased, the content in feces of

chenodeoxycholic acid is decreased, the content in feces of ursodeoxycholic acid is

decreased, or the content in feces of propionate is decreased, compared to the control

group untreated with the test substance.

The method for predicting a degree of risk of nonalcoholic fatty liver disease,

the method for providing information for prediction, the method for diagnosis, or the

method for providing information for diagnosis, according to one example of the present

invention may further comprise administering a therapeutic agent of nonalcoholic fatty

liver disease to a subject.

Other embodiment of the present invention relates to a method for treatment of

nonalcoholic fatty liver disease, comprising administering a therapeutic agent of

nonalcoholic fatty liver disease to a subject determined as having risk of nonalcoholic

fatty liver disease by the kit for predicting or diagnosing a degree of risk of nonalcoholic

fatty liver disease, the method for predicting a degree of risk of nonalcoholic fatty liver

disease, the method for providing information for prediction, the method for diagnosis,

or the method for providing information for diagnosis, according to the present

invention.

Any discussion of documents, acts, materials, devices, articles or the like which

has been included in the present specification is not to be taken as an admission that any

or all of these matters form part of the prior art base or were common general knowledge

in the field relevant to the present disclosure as it existed before the priority date of each

of the appended claims.

Throughout this specification the word "comprise", or variations such as

"comprises" or "comprising", will be understood to imply the inclusion of a stated

element, integer or step, or group of elements, integers or steps, but not the exclusion of

any other element, integer or step, or group of elements, integers or steps.

18A

[ADVANTAGEOUS EFFECTS]

The degree of risk of nonalcoholic fatty liver disease can be effectively

predicted or diagnosed using the kit for predicting or diagnosing of the present

invention, and in particular, the predictability and significance of information provision

in non-obese subjects are excellent. Therefore, it can be effectively used to prevent or

treat the corresponding disease by providing effective information on nonalcoholic fatty

liver disease through this.

[BRIEF DESCRIPTION OF THE DRAWINGS]

FIG. la to 11 are results of comparing the diversities of the gut microbiome.

Values dividing the alpha and beta diversity by histological spectrum of NAFLD or

fibrosis severity of all subjects are shown in FIG. la to Id, of non-obese subjects are

shown in FIG. le to lh, and of obese subjects are shown in FIG. li to FIG. 11.

Rarefaction curves were generated using the Shannon index with 12,000 sequences per

sample. Statistical analysis was performed using nonparametric Kruskal-Wallis test.

NMDS plots were generated using relative OTU abundance data according to

Bray-Curtis distance, and statistical significance was determined using Adonis analysis.

P<0.01

FIG. 2a to 2d are univariate analysis results for differences in specific microbial

taxa according to the severity of fibrosis. For clarity, 13 family- and 14 genus-level taxa

are shown along with their upper relative abundance. The box plot shows the

interquartile range (IQR) between the first and third quartiles with Tukey whiskers. The color in the box indicates the severity of fibrosis. For statistical significance, a nonparametric Kruskal-Wallis test was used. *P<0.05, **P<0.01,***P<0.001

FIG. 2e to 2h are multivariate analysis results for differences in specific

microbial taxa according to the severity offibrosis. Arcsine root-modified abundance of

four bacteria was regressed for age, gender and BMI according to the severity of

fibrosis, and the standard residual was indicated as a box plot. *P<0.05, **P<0.01,

P<0.001

FIG. 2i to 2k are results of co-expression analysis of specific gut microbiota

elements in total, non-obese and obese subjects. Solid lines (orange) and dotted lines

(grey) indicate positive and negative correlations, respectively. The size of the node

indicates the relative amount of bacteria, and the color indicates the degree of

correlation according to the severity offibrosis.

Fig. 3a to 3j are evaluation results of fecal metabolites mainly related to the gut

microbiota. FIG. 3a is the bile acid profile result in various clinical environments, and

stacked plots were generated using the average abundance of five bile acids. In FIG. 3b

to 3g, the box plots show stratified fecal bile acid levels according to fibrosis severity

and obesity status. The concentrations of the five fecal bile acids were stratified by

fibrosis severity and obesity status. In FIG. 3h to FIG. 3j, the box plots show the most

abundant fecal SCFA (acetate, propionate and butyrate) stratified by fibrosis severity

and obesity status. The interquartile ranges (IQRs) between the first and third quartiles

are described as Tukey whiskers. For statistical significance, a nonparametric

Kruskal-Wallis test was used. *P<0.05, **P<0.01,***P<0.001

FIG. 4 is the network profile result between microbial taxa and fecal metabolite

components in the non-obese (a) and obese (b) subjects. Co-expression coefficients

between family-level microbiota elements and fecal metabolites were calculated using

SparCC and described using Cytoscape. Solid lines (orange) and dotted lines (grey)

indicate positive and negative correlations, respectively. The shape of the node indicates

the components used in the present study (oval: microbiota, diamond: fecal bile acid,

and round rectangle: SCFA), and the color indicates the degree of correlation according

to the severity offibrosis.

FIG. 5a and FIG. 5b are receiver operating characteristic curves (ROC) for

prediction of significant fibrosis in total, non-obese and obese subjects. FIG. 5a is the

ROC curve using the combination of three selected bacteria (Veillonellaceae,

Ruminococcacea, and Enterobacteriaceae) and four fecal metabolites (CD, CDCA,

UDCA, and propionate) drawn for prediction of significant fibrosis in all the non-obese

subjects and obese subjects, and the areas under the curve (AUC) was calculated. FIG.

5b is the ROC curve using the combination of two selected bacteria (Megamonas and

Ruminococcus) and four fecal metabolites (CD, CDCA, UDCA, and propionate) drawn

for prediction of significant fibrosis in all the non-obese subjects and obese subjects,

and the areas under the curve (AUC) was calculated.

FIG. 6 is a schematic diagram that comprehensively summarizes the contents

corresponding to the differences in the gut microbiome, changes in microorganisms and

metabolites, and the prediction result of fibrosis through the combination of

representative microorganisms and metabolites, shown in the non-obese subjects and obese subjects.

FIG. 7 is a diagram showing the histological distribution of study subjects

stratified by fibrosis severity.

FIG. 8 is a diagram showing the correlation between the microbial taxa and

metabolic indexes in the non-obese and obese patients.

FIG. 9a to 9e are diagrams showing the correlation between the microbial taxa

and metabolic indexes in the non-obese and obese patients.

FIG. 10a to 10c are diagrams showing the relationship between the relative

abundance of specific gut microbial taxa and the severity of fibrosis at the genus level

stratified by the degree of obesity.

FIG. 11 is a diagram showing the relationship between the relative abundance

of Actiomyces stratified by the degree of obesity and the TM6SF2 (rs58542926)

variant.

FIG. 12a to 12d are diagrams showing the relationship between the relative

abundance of specific gut microbial components and the presence or absence of diabetes

mellitus.

FIG. 13a to 13e are diagrams showing the relative abundance of fecal bile acid

stratified by the severity offibrosis and the degree of obesity.

[MODE FOR INVENTION]

Hereinafter, specific Examples are provided to help the understanding of the

present invention, but the following Examples are only illustrative of the present

invention, and it is apparent to those skilled in the art that various changes and modifications are possible within the scope and spirit of the present invention, and it is also obvious that these changes and modifications fall within the scope of the appended claims. In the following Examples and comparative Examples, "%" and "part" indicating the content are by weight unless otherwise specified.

The values presented in the following experimental Example are expressed as

means standard deviation (S.D.), and the statistical significance of the difference

between each treatment group was determined by one-way ANOVA using Graph Pad

Prism 4.0 (San Diego. CA).

[Experimental example 1]

1. Material and method

1) Experimental subject

171 subjects demonstrated by biopsy to have NAFLD and 31 subjects without

NAFLD were included. When NAFLD was confirmed histologically and BMI was

BMI<25 kg/m 2, it was classified as the non-obese NAFLD group.

2) Subject inclusion and exclusion criteria

Subjects were enrolled long-term from January 2013 to February 2017, and the

inclusion criteria were as follows:

1. An adult at least 18 years of age,

2. Ultrasonic findings confirming fatty infiltration of liver, and

3. An increase of alanine aminotransferase (ALT) level of unknown etiology within the past 6 months.

On the other hand, subjects who met any of the following criteria were

excluded:

1. Hepatitis B or C infection,

2. Autoimmune hepatitis, primary biliary cholangitis, or primary sclerosing

cholangitis,

3. Gastrointestinal cancers or hepatocellular carcinoma,

4. Drug-induced steatosis or liver damage,

5. Wilson disease or hemochromatosis,

6. Excessive alcohol consumption (male: >210g/week, female: >140g/week),

7. Antibiotic use within the previous month,

8. Diagnosis of malignancy in the past year,

9. Human immunodeficiency virus infection, and

10. Chronic disorders related to lipodystrophy or immunosuppression.

Non-obese and obese control groups included subjects without any suspicion of

NFALD (a) during evaluation of living donor liver transplantation or (b) during liver

biopsy for characterization of solid liver mass suspected for hepatic adenoma or focal

nodular hyperplasia based on imaging studies (Koo BK, Joo SK, Kim D, Bae JM, Park

JH, Kim JH, et al. Additive effects of PNPLA3 and TM6SF2 on the histological

severity of non-alcoholic fatty liver disease. J Gastroenterol Hepatol

2018;33:1277-1285.).

3) Liver histology

Liver histology was evaluated by a single liver pathologist using the NASH

CRN histological scoring system. NAFLD was defined as the presence of >5%

macrovesicular steatosis based on histological examination. NASH was defined based

on the overall pattern of liver damage consisting of steatosis, lobular inflammation or

ballooning of hepatocytes according to the criteria of Brunt et al. (Brunt EM, Janney CG,

Di Bisceglie AM, Neuschwander-Tetri BA, Bacon BR. Nonalcoholic steatohepatitis: a

proposal for grading and staging the histological lesions. Am J Gastroenterol

1999;94:2467-2474; Brunt EM, Kleiner DE, Wilson LA, Belt P, Neuschwander-Tetri

BA. Nonalcoholic fatty liver disease (NAFLD) activity score and the histopathologic

diagnosis in NAFLD: distinct clinicopathologic meanings. Hepatology

2011;53:810-820). In addition, steatosis, hepatic lobular inflammation and swelling

were scored according to the NAFLD activity scoring system, and the severity of

fibrosis was evaluated according to the criteria of Kleiner et al. (Kleiner DE, Brunt EM,

Van Natta M, Behling C, Contos MJ, Cummings OW, et al. Design and validation of a

histological scoring system for nonalcoholic fatty liver disease. Hepatology

2005;41:1313-1321).

4) Microbiome analysis using 16S rRNA sequencing

DNA of the fecal sample was extracted using QIAamp DNA Stool Mini Kit

(Qiagen, Hilden, Germany). V4 region sequencing targeting of 16S rRNA was

performed using MiSeq platform (Illumina, San Diego, CA, USA), and additional

treatment of raw sequencing data was performed using QIIME pipeline (v 1.8.0)

(Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al.

QIIME allows analysis of high-throughput community sequencing data. Nat Methods

2010;7:335-336).

5) Measurement of fecal metabolites using GC-FID and Q-TOP system

Fecal SCFA was measured using Agilent Technologies 7890A GC system

(Agilent Technologies, Santa Clara, CA, USA) according to the method of David

(David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al.

Diet rapidly and reproducibly alters the human gut microbiome. Nature

2014;505:559-563), and the bile acid profile was evaluated using Q-TOF mass

spectrometer (Waters Micromass Technologies, Manchester, UK).

6) Bioinformatics analysis and statistical test

Statistical comparison was performed with Kruskal-Wallis test using GraphPad

Prism software Ver. 7.Od (GraphPad Software, San Diego, CA, USA). For rarefaction

curves, the OUT table was selected by 12,000 sequences per sample, and Shannon index

was measured by QIIME. Nonparametric multi-dimensional scaling (NMDS) plots were

represented using Vergan package of R (Oksanen J, Kindt R, Legendre P, O'Hara B,

Stevens MHH, Oksanen MJ, et al. The vegan package. Community ecology package

2007;10.), and the distance was measured using Bray-Curtis method. The statistical

significance between groups was estimated using Adonis function. Multivariate

association analysis using microbiome data was performed using multivariate

association using a linear model (MaAsLin) for identification of specific taxa related to

the host phenotype without being affected by other metadata (Morgan XC, Tickle TL,

Sokol H, Gevers D, Devaney KL, Ward DV, et al. Dysfunction of the gut microbiome

in inflammatory bowel disease and treatment. Genome Biol 2012;13:R79.). In addition,

age, gender and BMI or diabetes were designated as fixed variables, and when the

p-value adjusted by Benjamini and Hochberg's false discovery rate (FDR) was lower

than 0.20, the association rate was considered as significant.

7) Significant prediction of fibrosis by ROC curves

In order to demonstrate the prediction ability of fibrosis of the

microbiome-based biomarkers, the area under the receiver operating characteristic curve

(AUROC) method was used. The three family-level bacteria, basic characteristics of

subjects (age, gender and BMI) and relative abundance of FIB-4 confirmed in the

present experiment were used as inputs for AUROC, and the combination of their

factors was calculated using binary logistic regression in SPSS Ver. 25.0 (SPSS Inc.,

Armonk, NY, USA). AUROC comparison was performed by DeLong test using

MedCalc software Ver. 18.2.1 (MedCalc Software BVBA, Ostend, Belgium).

2. Experimental result

1) Basic characteristics

171 subjects demonstrated as NAFLD (NAFL, n=88; NASH, n=83) by biopsy

and 31 non-NAFLD subjects were included, and all subjects were divided into two

groups (non-obese, BMI<25; obese, BMI>25), and each subject was divided into three

subgroups according to the histological spectrum of NAFLD or fibrosis. In Table 1 and

Table 2, the result of detailed characteristics of each group including clinical, metabolic,

biochemical and histological profiles was shown.

[Table 1]

Baseline characteristics of study subjects stratified by obesity status and

histological spectrum of NAFLD.

Non-obese (n=64) Obese (n=138)

NFLD NAFL NASH P-value FLD NAFL NASH P-value

N (male/female) 7/14 13/11 7/12 4/6 37/27 24/40

Age (years) 58.7 +58.3 +60.2 ±0.8601 "s 58 12.6 52.7 53.6 0.6463 "s 10.7 10.2 8.84 14.8 16.7

BMI (kg/m2) 22.8 23.6 +23.6 0.0871 " 27 2.09 28.8 ±28.8 ±0.1374 1.67 1.34 0.83 3.25 3.02

WC (cm) 80 6.53a 2.7 85.4 4b 0.0141 * 92.73 96 0.2328 _________ 8_ _______ 5.93 18.04 7.81 022

31.6 +28.4 +54.7 0.002 ** 25.6 ±42.3 b 62 32 .3b 0.0001 AST (U/L) 24.4a 9 .0 5 ' 4 5 .7 ° 0 7.5a 26.5 32.5 32.8 5 0.000127.8 +56.9 +79.1 ± 0.0001 LT(UL) 32.6a 17 . 3 25952.5.8a 9c. 48.5b 57.6°

44.65431.8 +66.7 ** 44.7 ±49.2 +78.5 ± 0.0001 GGT(IU/L) 34.la 55 .2b 51.8 57.4 79 .2 HDL cholesterol 14 46.3 +43.8 ±0.0527 " 58.9 47 11.6b 45.5b 0.0196 (mg/dL) 10.8 11.7 14.3a 11.20 *

LDL cholesterol 93.5 111 39.8 0.5322 "s 123 35.8 103 32.3 107 32.7 0.1782 "s (mg/dL) 25.7 .32.1 ________

Albumin (g/dL) 4.1 +4.24 +4.03 0.1128 "s 4.12 +4.2 +4.15 0.516 "

0.285 0.257 0.413 0.312 0.254 0.279

Platelet (x 10 3/pL) 2 2a 594a 179 + 79 bc 0.0023 ** 232 53 234 59.9 215 69 0.3027 "s

103 +s 638 +113 7 +159 + 0.0026 *

Ferritin (ng/mL) 109 80.2 173 114 0.1285 " 1 157.2 ±10 +. 80.217 285

HA (ng/mL) 66.6 +37.1 +93.9. bc 0.0048 ** 76.8 +62.1 95.4a 95.7 1 12b 0.0344 03 7 0 .2 ab 30.2a 0 9 9 .2 ab

* 64 9

Insulin (pIUmL) 9.45 +11.2 ab +13.6 . b ± 0.037 * 11.2 5.55a +18.1 17 .1ab +18.2 1 1 .1 ± 0.0 0.0227 3.77a 5 .9 6

* 5 85

HbAle (%) 5.71 +6.06 +7.12 b 0.00015.72 +6.1 a 6.6 ±1.27b 0.0099* 0.481a 0.676a 1 .9 6 0.326a 0 .8 14ab

C-peptide(ng/mL)1.91 +2.43 +2.88 2.42 ±4.42 ±4.19 0 ** ________________ 0.61a 0 . 8 3 2ab 1 . 0 9b 0.0005 0.916a 3 .4b 3 bc 0.0087 HOMA-IR 2.56 +3.12 +4.39 0.0085 0 ** 2.92 1.77a 4.96 4. ab ±5.77 4 .42b 0.0131* 01 1.17a 1 . 7 2 ab . bc 2 19 36

Adipo-IR 4.68 +6.6 +9.63 0.0027** 6.42 10.1 12.8 0.0096** 2.85a 3 . 5 9 ab 5. 39 °bc 0 3.28a 1 0 .1 ab 9 . 51 0.00 FFA (pEq/L) 493ab 620 240a 0.0121 89a ± 9 ab 31bc 0.0089

hsCRP (mg/dL) 0.249 +0.0896 +0.354 0.0119* 0.152 +0.206 0.403a +0.278 0 . 3 3 6b 0.0294* 02 0.428a 0.066a . b 0 549 0 . 15 4 ab Cholesterol 167 185 41.8 0.3932 n 200 43.2 183 34.8 181 40.7 0.4122ns (mg/dL) 28.2 42.9 ___ ___ ____

TG (mg/dL) 102 47a 140 +141 0.0105 * 87 34a 161 151 bc 0.0024 002 ** 44.7______ b___ _______ 8 2 .2 b 6 1 9.

FPG (mg/dL) 256 113 28.8 0.0986 "" 102 14.1 113 27.4 128 55.8 0.2074 "s 1 42.6.5 _ 1__ _ _ 1__ _ _ _

HTN, n (%) 8(38.1) 8(33.3) 9(47.4) 0.641 "' 4(40.0) 4 (37.5) 32 (50.0) 0.352 ", Diabetes, n (%) 1 (4.76) 8 (33.3) 13 (68.4) 0.0001 *** 1 (10.0) 19 (29.7) 30 (46.9) 0.026* Abbreviations: BMI, body mass index; WC, waist circumference; AST, aspartate

transaminase; ALT, alanine transaminase; GGT, gamma-glutamyl transferase; NAS,

nonalcoholic fatty liver disease activity score; HDL, high-density lipoprotein; LDL,

low-density lipoprotein; HA, hyaluronic acid; HbAlc, glycosylated hemoglobin;

HOMA-IR, homeostasis model assessment of insulin resistance; Adipo-IR, adipose

tissue insulin resistance; FFA, free fatty acid; hsCRP, high-sensity C-reactive protein;

TG, triglycerides; FBG, fasting blood glucose; HTN, hypertension. Data are expressed

as the mean SD or n (%). Mean SD or n (%) with defferent superscript letters

indicates significant differences by the nonparametric Kruskal-Wallis test or the

chi-square test. *P<0.05, **P<0.01, ***P<0.001

[Table 2]

Baseline characteristics of study subjects stratified by obesity status and fibrosis

severity.

Non-obese (n=64) Obese (n=138) Fibrosis stage 0 1 2 P-value 0 1 2 P-value N (male/female) 27 (11/16) 20 (9/11) 17 (7/10) 25 (17/8) 73 (38/35) 40 (10/30) 57.67 +57.85 +62.47 57.08 ±48.36 +60.63 +0.0001 Age (years) 9.01 11.80 8.32 ).2371 "1 1 2 .4 1 ab 15.70a 1 3 .5 6 b ***

BMI (kg/m 2) 22.81 ±23.76 +23.71 ±).0084 27.48 ±29.30 2r8.27 0.0119 1.42a . 0 .9 2 ab 2_.9 7 ab

* 1 4 6b 2.58a 3.20 79.95 83.22 +86.33 .0010 91.82 +96.16 +95.73 07 WC (cm) 5.2a 3 .5 3 ab 4.22 * 6.56 7.35 8.98 0.0074 29.11 +32.20 +56.06 ±0.0017 28.40 ±48.99 +66.13 < 0.0001 AST (IU/L) 21.64a 1 0 .6 3 ab 48. 1 3 ** 13.03a 26. 7 4 b 36.43° ***

31.89 +39.10 +55.71 0.0886 "s38.88 +70.85 +70.85 +0.0003 ALT (IUL) 52.04 43.67a 5 3 .0 2be 56.63° 27.83 30.99 33.6 ±44 +69.7 ±0.0046 a57.5 +85.9 +0.0016 GGT(JUL) 41.4a 46.2ab 5 8 .3b 39.6±41 5 6 .1ab 9 5 .2b

HDL-cholesterol 51.4 ±47.9 ±43.1 3.1701 "s48.4 13.147.2 12 46.3 0.9175

" (mg/dL) 13.4 11.1 12.3 11.5 LDL-cholesterol 104 29.9 108 38.6 90.8 3.3250 ns105 35.7 109 31.7 102 33.8 0.5024 ns (mg/dL) 32.3

414 +3.99 +.1781 "s4.12 +4.24 4.08 +0.0027 Albumin (g/dL) +4.22 0.25 0.29 0.43 0. 2 4ab 0.26a 0.27 +183.88 +.0255 * 238.2 +241.44 +188.33 +0.0001 Platelet (X103/pL) 230.19 +247.75 55.38a 62.49a 5 8 .0 5 b 48.76a 74.84a 9 5 .3 5 b

Ferritin (ng/mL) 117.75 +100.94 +282.19 .053 "s 145.55 +219.37 +169.26 +0.8403

" 73.97 74.93 386.91 89.51 255.97 133.32 33.08 +64.2 +109.59 + .0002 51.32 +61.58 +127.28 +0.0001 HA (ng/mL) 19.62a 67.47a 6 5 .5 6 b *** 66.4a 90.48a 1 2 9 .4 *

10.76 +10.05 +13.71 3.1103 "s14.22 +17.83 +19.50 0 Insulin (pIUmL) 0.0148 5.57 4.15 6.19 11.57a 1 5 .4 6ab 12 .5 8b * 5.86 +5.98 +7.23 ±0.0007 5.87 +6.15 +6.87 +0.0007 HbAlc(%) 0.69a 0 .4 4 ab 2.05° ** 0.54a 0 .8 5 ab 1.42°

C-peptide 2.23 2.23 2.85 0.0256* 3.22 ±4.43 ±4.29 0.0498* (ng/mL) 0.87a 0 .6 4 ab bc 1 17 1.57a 3 .4 3 ab 2.28b° 2.94 ±2.81 ±4.50 . * 3.84 ±4.82 +6.69 +0.0006 HOMA-IR 1.61a 1 3 b.0207 1.33ab 2.2 8b 0.1 3.35aa35 4.118ec 4.70o 5.72 +6.33 +9.49 . a 10.76 ±13.86 +0.0043 9 ~ab Adipo-IR 3.1 3.01 370.5 3.72 5.95 .0645 s7.48±6.2 9.7a 10.55°be 553.96 +615.65 +684.88 0 ns556.08 +642.76 +737.1 +0.0059 186.66 238.13 308.55 3.2678 209.52a 2 5 7 .6 1 ab 2 5 9 .4 2 **

0.17 +0.23 +0.29 . * 0.14 +0.23 +0.3 0 hsCRP (mg/dL) .0186 * 013 a b 7b 0.0121 *

4 ab 08 c 0.33a 0.41ab 0.48 0.173 0.39 0.3 Cholesterol 177.7 28180.75 +158.53 3.1860"s180.96 188.58 4175.33 0.2143 (mg/dL) 43.68 44.94 38.04 34.01 44.67

+137.12 0.9889 "s127.36 +156.23 +155.43 0 TG (mg/dL) 120.70 +128.42 45.23 51.84 77.04 51.42 80.68 67.19 111.15 +111.85 +134.47 . * 110.96 107.82 +144.53 0.0001 FPG(mg/dL) 31.51a 1 9.5 8 ab 43. 7 9 b 0402 34.36a 1.43a 6 3 .5 3 b

HTN,n(%) 7(25.9) 9(45.0) 9(52.9) .163 ", 9(36.0) 30(41.1) 1 (52.5) 0.357 "s

Diabetes, n (%) 4 (14.8) 5 (25.0) 13 (76.5) -0001 3 (12.0) 23 (31.5) 24 (60.0) 0.0002

As a result of confirmation, subjects with NASH or significant fibrosis (F2-4)

had high levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT)

and diabetic markers in all obese and non-obese groups. The subjects with significant

fibrosis had higher NAFLD activity scores, and showed more severe liver histology in

terms of histological classification of NAFLD (Table 3 and FIG. 7). More detailed

standard characteristics of each fibrosis stage, comprising well-known NAFLD-related

genetic variations such as PNPLA3, TM6SF2, MBOAT7-TMC4, and SREBF-2 were

shown in Table 4.

[Table 3]

Histological characteristics of study subjects stratified by obesity status and

fibrosis severity.

Non-obese (n=64) Obese (n=138) Fibrosis stage 0 1 2 0 1 2 Steatosis, n (%) 0 (<5%) 15 (55.6) 5 (25.0) 1 (5.9) 7(28.0) 2(2.7) 1 (2.5) 1 (5-33%) 7(25.9) 6(30.0) 8(47.1) 12(48.0) 9(12.3) 13 (32.5) 2(34-66%) 4(14.8) 7(35.0) 2(11.8) 3(12.0) 29(39.7) 12(30.0) 3 (>66%) 1 (3.7) 2(10.0) 6(35.3) 3(12.0) 33(45.2) 14(35.0) Lobular inflammation, n (%) 0 15 (55.6) 3 (15.0) 1 (5.9) 13 (52.0) 5 (6.9) 3 (7.5)

1 12(44.4) 14(70.0) 11 (64.7) 12(48.0) 60(82.2) 30(75.0) 2-3 0 3 (15.0) 5(29.4) 0 8(11.0) 7(17.5) Ballooning, n (%) 0 22(81.5) 7 (35.0) 0 22(88.0) 18(24.7) 5 (12.5) 1-2 5 (18.5) 13 (65.0) 17 (100.0) 3 (12.0) 55 (75.3) 35 (87.5)

Histological classification, n (%) No NAFLD 15 (55.6) 5 (25.0) 1(5.9) 7(28.0) 2(2.7)

' NAFL 11 (40.7) 13 (65.0) 0 18(72.0) 40 (54.8) 7(17.5) NASH 1 (3.7) 2 (10.0) 16(94.1) 0 31 (42.5) 3(82.5) NAS 1.30 1.46 2.95 1.61 4.00 1.32 1.68 1.22 4.11 1.23 .08 1.10 Abbreviations: NAFLD, nonalcoholic fatty liver disease; NAFL, nonalcoholic fatty

liver; NASH, nonalcoholic steatohepatitis; NAS, NAFLD activity score. Date are

expressed as the mean SD or n().

[Table 4]

Baseline clinical, metabolic, histological, and genetic characteristics of study

subjects stratified by obesity status and fibrosis stage.

Non-obese (n=64) Obese (n=138) Fibrosis stage 0 1 2 3 4 0 1 2 3 4

N (male/female) 27 20 9(5/4) 4(1/3) 4 (1/3)25 (17/8) 33 5 7(2/5) 13 (11/16) (9/11) (38/35)________ (5/15) (3/10) 57.7 +57.8 +58.6 +67.2 +66.5 57.1 +48.4 +55.6 +65.6 +65.8 Age (years) 9.01 11.8 9.9 2.5 1.73 12.4 15.7 16.4 8.89 6.92 22.8 +23.8 +23.5 +23.9 +24 +27.5 +29.3 +28.5 +28 +28 BMI 1.42 1.46 0.862 1.33 0.66 2.58 3.2 3.26 3.43 2.39

WC (cm) 79.9 +83.2 +85.5 +88.1 +86.1 91.8 +96.2 +95.8 +95.8 +95.6 5.2 3.53 2.27 4.3 8.37 6.55 7.35 9.02 13.2 7.76 128 +127 +132 +158 +124 130 +136 +133 +132 +126 SBP (mm Hg) 16.9 14.3 17.1 37.4 21.7 14.8 18.4 17 11.3 17.9 76.8 +77.4 +80.2 +86.8 +74.5 80 +84.1 +77.4 +77 +74.3 DBP (mm Hg) 12.8 8.26 12.9 19.1 10.8 9.78 11.8 13.4 10.8 9.55 29.1 +32.2 +55.1 +80.5 +33.8 28.4 + 9 +66.6 +70.4 +63 AST (IUL) 21.6 10.6 55.8 50.1 8.34 13 6.7 46.5 27.9 21.7 31.9 +39.1 +52.6 +93.5 +25 +38.9 +70.8 +87.2 +58 +52.6 ALT (IUL) 27.8 31 53.2 60.5 7.53 43.7 53 74.3 19.3 24.6 33.6 +44 +69.7 +373 +63 +39.6 +57.5 +79.4 +86.6 +95.6 GGT (IUL) 41.4 46.2 64 592 43.8 1 56.1 86.1 68.5 123 10.8 +10 +15 +15.3 +9.32 14.2 +17.8 +21.3 +13.5 +20 Insulin (pIUmL) 5.57 4.15 6.14 7.18 4.29 11.6 15.5 16.1 4.73 8.1 5.86 +5.98 +6.8 +6.05 +9.38 5.87 +6.15 +7.01 +6.79 +6.74 HbAle +

0.688 0.445 0.689 0.557 3.52 0.538 0.851 1.45 1.15 1.59 2.94 +2.81 +4.57 +4.56 +4.27 3.84 +4.82 +7.28 +4.4 +7.01 HOMA-IR +

1.61 1.33 2.05 2.04 3.5 3.36 4.18 5.79 1.42 3.71 5.72 +6.33 +11.1 +8.94 +6.44 7.48 +10.8 +14.3 +9.29 +15.1 Adipo-IR +

3.01 3.72 6.82 5.18 4.2 6.2 9.7 12.9 7.57 7.42

Diabetes, n(%) 4 (14.8)5 (25.0) 7 (77.8)2 (50.0)(100) 3(12.0) 231 ( (57.1) 8 (100)_ (31.5) 2(6070) )(61.5) Albumin (g/dL) 4.15 +4.22 +4.19 +3.92 +3.62 +4.12 +4.24 +4.11 +4.06 +4.04 +

0.259 0.291 0.285 0.45 0.499 0.243 0.26 0.192 0.207 0.393 +248 +222 +206 +76.8 238 ±241 +206 +195 +157 ± Platelet (X103/pL) 230 48.8 74.8 67.2 123 32.1 55.4 62.5 50.6 48.8 63.7 121 +128 +180 +82.5 +96 +127 +156 +175 +140 +134 ± TG (mg/dL) 45.2 51.8 82.9 26.6 31.1 51.4 80.7 71.5 48.6 64.5 11 ±112 +123 +123 +172 +111 +108 +148 +138 +143 ± FPG (mg/dL) 31.5 19.6 26.7 37 66.6 34.4 21.4 78.3 30.6 53.9 Histological classification

No NAFLD (55.6) 5 (25.0)0 1 (25.0)0 7(28.0) 2(2.7) 0 0 0

NAFL(40.7) (65.0) 0 0 0 18(72.0)(4. 6(30.0)1 (14.3)0

NASH 1 (3.7) 2 (10.0) 9 (100) 3 (75.0) 100) 0 31 1 6(85.7) 13 (0)(42.5) (85.7) (100) Genetic variants

NPLA3 G/G 6 (22.2)4 (20.0)1 (11.1)0 50.0) 4(27.4) 2 55.0) 2(28.6) 6 (rs738409) C/G 13 13 5 (55.6)1 (25.0) 2 11 (44.0) 3 4 (20.0)4 (57.1) 3 (38.1) (65.0) (50.0) (47.9) (71(30.8) C/C 7 (25.9) 3 (15.0)2 (22.2)3 (75.0)0 7(17.8)17.8) 4 (20.0)1 (14.3) 1 (7.7)

c/c 21 18 6 (66.7)2 0 18(72.0) 56 16 4(57.1) 10 TM6SF2 (77.8) (90.0) 667 (75.0) 1 (76.7) (80.0) 4 (76.9) (rs58542926) C/T 5 (18.5)2 (10.0)2 (22.2)2 (50.0) (25.0) 4(16.0) (15.1) 3 (15.0)2 (28.6) 1 (7.7)

T/T 0 0 0 0 0 0 1(1.4) 0 1 (14.3)0 BOTTCC/C 17 13 5(561(504 156.)42 11 3(297 MBOAT7-TMC4 (63.0) (65.0) 5(55.6)1(25.0)(100) (60.0)(57.5) (55.0) (42.9)(53.8) (rs641738) C/T 9 (33.3) 5 (25.0) 3 (33.3) 3 (75.0) 0 4(16.0) 28.8) 8 (40.0) 4 (57.1) 308)

T/T 0 2(10.0)0 0 0 3(12.0) 5 (6.8) 0 0 0

C/C 7 (25.9)6 (30.0)1 (11.1)2 (50.0) 500) 6(24.0) 3 8(40.0)1 (14.3) 3 SREBF-2 12 227 5 (rs133291) C/T (4)8 (40.0)6 (66.7) 1 (25.0) 50.0) 7(28.0) (370) 5 (25.0)5 (71.4) (38.5)

T/T 3 (11.1)16 (30.0)1 (11.1)1 (25.0)0 3(12.0) 8 (11.0) 4 (20.0) 1 (14.3) 2

Abbreviations: BMI, body mass index; WC, waist circumference; SBP, systolic blood

pressure; DBP, diastolic blood pressure; AST, aspartate transaminase; ALT, alanine

transaminase; GGT, gamma-glutamyl transferase; HbAlc, glycosylated hemoglobin;

HOMA-IR, homeostasis model assessment of insulin resistance; Adipo-IR, adipose

tissue insulin resistance; TG, triglycerides; FBG, fasting blood glucose; NAFLD,

nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PNPLA3,

patatin-like phospholipase domain-containing protein 3; TM6SF2, transmembrane 6 superfamily 2; MBOAT7-TMC4, membrane bound 0-acyltransferase domain-containing 7 gene and transmembrane channel-like 4 gene; SREBF-2, sterol regulatory element binding transcription factor 2. Data are expressed as mean SD or n

().

2) Observation of changes in microbiome according to fibrosis severity

Depending on the fibrosis severity, changes of the microbiome were shown

differently in the non-obese NAFLD subjects and obese NAFLD subjects.

Specifically, the microbial diversity was compared according to the histological

spectrum of NAFLD or fibrosis severity (FIG. 1). For comparison of alpha diversity,

rarefaction curves based on Shannon metric were plotted, and NMDS plots based on

Bray-Curtis distance were plotted for beta diversity. As a result of confirmation, any

significant changes between groups stratified by the histological spectrum ofNAFLD or

fibrosis severity were not found in the merged subjects (FIG. la to Id).

The subjects were classified into two groups according to their BMI status. In

the non-obese group, a significant decrease in microbial diversity was observed between

Fl and FO (p=0.00 7 4 ), as well as between F2-4 and FO (p=0.00 8 4 ) (FIG. le to lh).

Moreover, clear clustering between FO and F2-4 was observed (p=0.03 8 ). In the obese

group, there was no significant change in diversity between groups stratified by the

histological classification of NAFLD or fibrosis severity (FIG. li to 11).

The result indicates that the fibrosis severity is more related to gut microbiome

change than necroinflammatory activity, and basic BMI status may also be an important factor contributing to gut microbiome change.

3) Observation of proliferation of fibrosis-related microbial taxa

Proliferation of the fibrosis-related microbial taxa was remarkably shown in the

non-obese NAFLD subjects. Specifically, in the non-obese and obese subjects, the

differences of the specific microbial taxa according to the fibrosis severity were

compared using univariate and multivariate analyses (FIG. 2a to 2d and 2e to 2h).

In the univariate analysis, not only gradual proliferation of Veillonellaceae

mostly found in the oral cavity and small intestine and large intestine, but also

Enterobacteriaceae were observed according to the fibrosis severity of the non-obese

subjects. In the obese subjects, Rikenellaceae became gradually enriched. On the

contrary, the abundance of Ruminococcaceae was significantly reduced as fibrosis

became more severe, and this was found only in the non-obese subjects. This result

could be confirmed in correlation plots (FIG. 8), and Enterobacteriaceae and

Veillonellaceae showed a positive correlation with the fibrosis severity (p=1.09x10-4 ,

p=2.44x10-3, respectively), but Ruminococcaceae showed an inverse correlation.

At the genus level, Faecalibacterium (Ruminococcaceae), Ruminococcus

(Ruminococcaceae), Coprococcus (Lachnospiraceae), and Lachnospira

(Lachnospiraceae) were significantly drastically reduced in the significant fibrosis

group, but the abundance of Enterobacteriaceae_Other (Enterobacteriaceae) and

Citrobacter was gradually increased according to the fibrosis severity. This change was

observed only in the non-obese subjects.

For multivariate analysis, the age, gender and BMI were adjusted using

MaAsLin. Enterobacteriaceae was an abundant family significantly related to the

fibrosis severity in the non-obese subjects (p=0.0108, q=0.214) (FIG. 2e to 2h). In

phylum Firmicutes, Veillonellaceae showed a steep increase of the relative abundance

in the non-obese subjects than the obese subjects (non-obese, p=0.0002, q=0.0195), but

the abundance of Ruminococcaceae showed an inverse correlation with the fibrosis

severity in the non-obese subjects (p=0.0019, q=0.0908). A representative genus of

Ruminococcaceae, Ruminococcus also showed a significant inverse correlation

according to the fibrosis severity (p=0.0009, q=0.135) (FIG. 10a to 10c). In addition,

Veillonellaceae and Enterobacteriaceae showed a significant positive correlation with

the serum free fatty acid (FFA) level in the non-obese subjects (q=0.178, q=0.118,

respectively), but it did not in the obese subjects (FIG. 9a to 9e).

Adipo-IR and glycosylated hemoglobin (HbAlc) also showed a positive

correlation according to the abundance of Veillonellaceae (adipo-IR, q=0.142; HbAlc,

q=0.157). On the contrary, the serum FFA level showed an inverse correlation with the

abundance of Ruminococcus in all subjects (q=0.0838) and non-obese subjects

(q=0.0838), but it did not in the obese subjects (q=1.00).

In order to elucidate whether these remarkable microbiome changes in the

non-obese subjects are related to the host gene effect, the association between bacteria

and genetic mutations of PNPL3, TM6SF2, MBOAT7-TMC4, and SREBF-2 using

MaAsLin was analyzed. However, significant association of the four genetic mutations

with three bacteria was not observed. Only actinomyces enriched the minor allele of

TM6SF2 (C/T) (q=O.169) in the non-obese subjects (FIG. 11).

In addition to the three variables of age, gender and BMI, the presence of type 2

diabetes mellitus (DM) was well known to affect the general changes in the microbiome

(Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study

of gut microbiota in type 2 diabetes. Nature 2012;490:55-60.). After additional

adjustment for DM, it was found that Enterobacteriaceae (p=0.00197, q=0.0616) and

Faecalibacterium (p=0.00 2 4 2 , q=0.0707) were related to the presence of DM in all the

subjects (FIG. 12a to 12d). In the non-obese subjects, not only depletion of Lachnospira

(p=5.26x10-4 , q=0.0676), but also the proliferation of Klebsiella (p=0.003 3 9 , q=0.141)

belonging to Enterobacteriaceae in the obese subjects were also observed in the DM

subjects.

In order to understand the interaction between the microbial components and

gut microbiota network characteristics in the obese and non-obese subjects,

co-expression of the taxa related to the fibrosis severity was measured, and the relative

abundance was shown (FIG. 2i to 2k).

As a result, in the non-obese subjects, Veillonellaceae and Enterobacteriaceae

had an inverse correlation with Ruminococcaceae (rho=-0.275 and -0.333, respectively),

and Prevotellaceae showed an inverse correlation with Bacteroidaceae (rho=-0.391).

However, the strong interaction between Veillonellaceae/Enterobacteriaceae and

Ruminococcaceae was not observed in the obese and all subjects. In particular, the

correlation of Veillonellaceae and fibrosis severity was not significant in the obese

subjects and all subjects, and this suggests its specific role in progression of fibrosis in the non-obese subjects.

In summary, the proliferation of the specific taxa according to the fibrosis

severity was more pronounced in the non-obese group than in the obese group.

4) Observation of fecal metabolite level according to fibrosis severity of

non-obese and obese NAFLD subjects

The non-obese and obese NAFLD subjects had different fecal metabolite levels

according to the fibrosis severity. Specifically, fecal metabolites mainly related to the

gut microbiota were evaluated.

The composition of the total bile acid pool between the non-obese and obese

subjects was various, and the non-obese subjects had an increased primary bile acid

level according to the increased fibrosis stage (FIG. 3a).

The total fecal bile acid level was 3 times higher in the non-obese subjects

having significant fibrosis (F2-4) than the subjects without fibrosis (FO) (FIG. 3b to FIG.

3g). In particular, the cholic acid (CA), chenodeoxycholic acid (CDCA), and

ursodeoxycholic acid (UDCA) levels were increased according to the fibrosis severity

increased in the non-obese subjects (FIG. 3b to FIG. 3g and FIG. 13a to 13e). The

lithocholic acid (LCA) and deoxycholic acid (DCA) levels were significantly high in the

obese subjects having significant fibrosis, and only lithocholic acid showed significant

improvement after percentage display.

Among three SCFA, the fecal propionate level was gradually increased as

fibrosis became severe in the non-obese subjects (non-obese; p=0.0032, obese; p=0.7979), and showed a significantly positive correlation with the amount of

Veillonellaceae known as propionate-producing bacteria (p=0.0155) (FIG. 3h to 3j).

On the contrary to the bile acid profile, the correlation between the significant

change of fecal SCFA and its bacterial taxa was observed only in the non-obese subjects

(FIG. 4b). Ruminococcus (p=0.0189), Oscillospira (p=1.57x10-4 ) and Desulfovibrio

(p=9.76x10-4) well-known as SCFA-producing bacteria showed an inverse correlation

with the fecal propionate level. The change in the fecal butyrate level according to the

fibrosis severity was not found in all the non-obese and obese subjects, and the

reduction of Ruminococcaceae did not affect the fecal butyrate level (non-obese,

p=O.597; obese, p=O.109).

5) Observation of bacterial taxa-metabolite network pattern in non-obese

and obese NAFLD

A bacterial taxa-metabolite network showed a unique pattern in the non-obese

and obese NAFLD. Specifically, when comparing the gut microbiota elements

according to the fibrosis severity and obesity status, a clear change in the microbiome

was observed only in the non-obese subjects. To investigate its core cause,

NAFLD-associated genetic variant and intestinal metabolite analysis was performed.

Based on the result, co-expression of the taxa and metabolites was evaluated,

and the interaction network was shown in FIG. 4. Strong interaction between bile acids

was observed in all the non-obese and obese subjects. However, the bacterial taxa and

metabolite co-expression pattern according to the fibrosis severity was different in the non-obese subjects and obese subjects: The non-obese subjects showed a more significant co-expression pattern than the obese subjects.

Interestingly, primary bile acid had an inverse correlation with

Ruminococcaceae and Rikenellaceae known as indexes of healthy intestine in all the

non-obese and obese subjects. Veillonellaceae exhibited a positive correlation with

propionate, as well as primary bile acid. Bile acid usually has the potential to regulate

growth of susceptible bacteria or to propagate relatively resistant bacteria regardless of

obesity status. Nevertheless, the correlation of the intestinal bacterial taxa and fecal

metabolites with the sever fibrosis was more remarkable in the non-obese NAFLD

subjects than the obese subjects.

6) NAFLD prediction of non-obese subjects by microbiota and metabolite

combination

The microbiota-metabolite combination accurately predicted significant fibrosis

in the non-obese NAFLD subjects. Specifically, in order to evaluate the usefulness as a

fibrosis-predicting biomarker of the gut microbiota and related fecal metabolites,

AUROC for predicting significant fibrosis was compared (FIG. 5a and FIG. 5b).

Enterobacteriaceae, Veillonellaceae, and Ruminococcaceae were selected as

most representative, and significant fibrosis-related bacterial taxa. As shown in FIG. 5a,

the combined bacterial marker to predict significant fibrosis yielded an AUROC of

0.824 in the non-obese subjects (0.661 for all subjects; 0.584 for obese subjects).

In addition, Megamonas belonging to Veillonellaceae family and

Ruminococcus belonging to Ruminococcaceae were selected. As shown in FIG. 5b,

AUROC to predict significant fibrosis was yielded as 0.718 (0.673 for all subjects;

0.648 for obese subjects).

As fibrosis-related metabolites, four fecal metabolites (cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid, and propionate) were selected, and the

combination of the four metabolites predicted significant fibrosis as AUROC of 0.758

in the non-obese subjects (0.505 for all subjects; 0.520 for obese subjects).

In case of addition of the intestinal metabolites to the bacterial marker at a

family level, as shown in FIG. 5a, the predicting ability was significantly enhanced as

improved AUROC of 0.977 (0.786 for all subjects; 0.609 for obese subjects). In

addition, in case of addition of the intestinal metabolites to the bacterial marker at a

genus level, as shown in FIG. 5b, it was enhanced as improved AUROC of 0.955 (0.590

for all subjects; 0.636 for obese subjects). The predictive ability of the novel

microbiota-metabolite biomarker was significantly higher than FIB-4 widely used as a

non-invasive biomarker ofNAFLD.

The result demonstrated that the diagnosis accuracy of the combination of the

identified intestinal bacterial taxa and fecal metabolite, for predicting significant fibrosis

in NAFLD subjects was significantly higher in the non-obese subjects than the obese

subjects, and clear differences of specific bacterial taxa and large intestine metabolite

between the obese and non-obese NAFLD groups could be confirmed. This result

emphasizes not only the importance of the gut microbiome as a risk factor explaining

the pathogenesis of non-obese NAFLD, but also the importance of application in diagnosis of the novel microbiome-metabolite combination as a non-invasive biomarker for significant fibrosis in non-obese NAFLD.

Claims

[CLAIMS]

[Claim 1]

A kit when used to predict or diagnose a degree of risk of nonalcoholic fatty liver

disease for a non-obese patient comprising a detection means detecting one or more detection

markers selected from the group consisting of

(a) one or more detection markers selected from the group consisting of microbial

biomarkers of nonalcoholic fatty liver disease, wherein the microbial biomarker is one or more

selected from the group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,

Citrobacter,Megamonas, Ruminococcus, Faecalibacterium,Coprococcus and Lachnospira;

(b) one or more detection markers selected from the group consisting of total bile acid

and components of bile acid, wherein the total bile acid and components of bile acid is one or

more selected from the group consisting of cholic acid, chenodeoxycholic acid and

ursodeoxycholic acid; and

(c) intestinal short chain fatty acid, wherein the intestinal short chain fatty acid is

propionate.
[Claim 2]

The kit according to claim 1, wherein the (a) microbial biomarkers of nonalcoholic

fatty liver disease are one or more selected from the group consisting of Enterobacteriaceae,

Veillonellaceae, and Ruminococcaceae.
[Claim 3]

The kit according to claim 1, wherein the (a) microbial biomarkers of nonalcoholic

fatty liver disease are one or more selected from the group consisting of Citrobacter,

Megamonas, Ruminococcus, Faecalibacterium, Coprococcus and Lachnospira.
[Claim 4]

The kit according to claim 1, wherein the kit comprises one or more selected from

combinations of (a) to (p) below:

(a) one or more detection means capable of detecting one or more selected from the

group consisting of Enterobacteriaceae, Veillonellaceae and Ruminococcaceae, respectively;

(b) one or more detection means capable of detecting one or more selected from the

group consisting of cholic acid, chenodeoxycholic acid and ursodeoxycholic acid, respectively;

(c) detection means capable of detecting propionate;

(d) one or more detection means capable of detecting one or more selected from the

group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid and propionate, respectively;

(e) one or more detection means capable of detecting one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira, cholic acid,

chenodeoxycholic acid and ursodeoxycholic acid, respectively;

(f) one or more detection means capable of detecting one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira and fecal

propionate, respectively;

(g) one or more detection means capable of detecting one or more selected from the

group consisting of Megamonas, cholic acid, chenodeoxycholic acid and ursodeoxycholic acid,

respectively;

(h) one or more detection means capable of detecting one or more selected from the

group consisting of Megamonas and fecal propionate, respectively,

(i) one or more detection means capable of detecting one or more selected from the

group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, Citrobacter,

Megamonas, Ruminococcus and Faecalibacterium, respectively;

() one or more detection means capable of detecting one or more selected from the

group consisting of cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and metabolites

thereof, respectively;

(k) detection means capable of detecting propionate, respectively,

(1) a detection means capable of detecting Enterobacteriaceae, Veillonellaceae, and

Ruminococcaceae,

(m) a detection means capable of detecting Megamonas and Ruminococcus,

(n) a detection means capable of detecting cholic acid, chenodeoxycholic acid,

ursodeoxycholic acid, and propionate,

(o) combination of (1) and (n), or

(p) combination of (m) and (n).
[Claim 5]

The kit according to claim 1, wherein the nonalcoholic fatty liver disease is

nonalcoholic fatty liver, nonalcoholic steatohepatitis, liver fibrosis or cirrhosis.
[Claim 6]

The kit according to claim 1, wherein the non-obese patient has BMI of 25 Kg/m2 or

less.
[Claim 7]

The kit according to claim 1, wherein the predicting the degree of risk is predicting the

severity of fibrosis comprising F=O to F=4.
[Claim 8]

A method for providing information for predicting or diagnosing a degree of risk of

nonalcoholic fatty liver disease for a non-obese patient comprising detecting one or more

detection markers selected from the group consisting of

(a) one or more detection markers selected from the group consisting of microbial

biomarkers of nonalcoholic fatty liver disease, wherein the microbial biomarker is one or more

selected from the group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,

Citrobacter,Megamonas, Ruminococcus, Faecalibacterium,Coprococcus and Lachnospira;

(b) one or more detection markers selected from the group consisting of total bile acid

and components of bile acid, wherein the total bile acid and components of bile acid is one or

more selected from the group consisting of cholic acid, chenodeoxycholic acid and

ursodeoxycholic acid; and

(c) intestinal short chain fatty acid, wherein the intestinal short chain fatty acid is

propionate.
[Claim 9]

The method according to claim 8, wherein the (a) microbial biomarkers of

nonalcoholic fatty liver disease are one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceae, and Ruminococcaceae.
[Claim 10]

The method according to claim 8, wherein the (a) microbial biomarkers of

nonalcoholic fatty liver disease are one or more selected from the group consisting of

Citrobacter,Megamonas, Ruminococcus, Faecalibacterium,Coprococcus and Lachnospira.
[Claim 11]

The method according to claim 8, wherein the method comprises one or more steps

selected from combinations of (a) to (p) below:

(a) comparing abundances of one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceae and Ruminococcaceae measured in a subject, with a

reference value of a healthy individual;

(b) comparing the contents in feces of one or more selected from the group consisting

of total bile acid, cholic acid, chenodeoxycholic acid and ursodeoxycholic acid measured in a

subject, with a reference value of a healthy individual;

(c) comparing the contents in feces of propionate measured in a subject, with a

reference value of a healthy individual;

(d) comparing abundances or the contents in feces of one or more selected from the

group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid and propionate measured in a subject, with a

reference value of a healthy individual;

(e) comparing abundances or the contents in feces of one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira, cholic acid,

chenodeoxycholic acid and ursodeoxycholic acid measured in a subject, with a reference value

of a healthy individual;

(f) comparing abundances or the contents in feces of one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira and feces

propionate measured in a subject, with a reference value of a healthy individual;

(g) comparing abundances or the contents in feces of one or more selected from the

group consisting of Megamonas, cholic acid, chenodeoxycholic acid and ursodeoxycholic acid

measured in a subject, with a reference value of a healthy individual;

(h) comparing abundances or the contents in feces of one or more selected from the

group consisting of Megamonas and feces propionate measured in a subject, with a reference

value of a healthy individual;

(i) comparing abundances of one or more selected from the group consisting of

Enterobacteriaceae, Ruminococcaceae, Citrobacter, Megamonas, Ruminococcus and

Faecalibacterium measured in a subject, with a reference value of a healthy individual;

(j) comparing the contents in feces of one or more selected from the group consisting

of cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and metabolites thereof measured

in a subject, with a reference value of a healthy individual;

(k) comparing the contents in feces of propionate measured in a subject, with a

reference value of a healthy individual;

(1) comparing abundances of Enterobacteriaceae, Veillonellaceae, and

Ruminococcaceae measured in a subject, with a reference value of a healthy individual;

(in) comparing abundances of Megamonas and Ruminococcus measured in a subject,

with a reference value of a healthy individual;

(n) comparing the contents in feces of cholic acid, chenodeoxycholic acid,

ursodeoxycholic acid and propionate measured in a subject, with a reference value of a healthy

individual;

(o) comprising (1) and (n); and

(p) comprising (in) and (n).
[Claim 12]

The method according to claim 8, wherein the nonalcoholic fatty liver disease is

nonalcoholic fatty liver, nonalcoholic steatohepatitis, liver fibrosis or cirrhosis.
[Claim 13]

The method according to claim 8, wherein the non-obese patient has BMI of 25 Kg/m2 or less.
[Claim 14]

A method for screening a therapeutic agent for nonalcoholic fatty liver disease for a

non-obese patient, comprising

(1) administering a test substance to a non-obese experimental animal having

nonalcoholic fatty liver disease;

(2) measuring one or more of detection markers selected from the group consisting of

(a) one or more detection markers selected from the group consisting of microbial biomarkers

of nonalcoholic fatty liver disease, wherein the microbial biomarker is one or more selected

from the group consisting of Enterobacteriaceae, Veillonellaceae, Ruminococcaceae,

Citrobacter,Megamonas, Ruminococcus, Faecalibacterium,Coprococcus and Lachnospira;(b)

one or more detection markers selected from the group consisting of total bile acid and

components of bile acid, wherein the total bile acid and components of bile acid is one or more

selected from the group consisting of cholic acid, chenodeoxycholic acid and ursodeoxycholic

acid; and (c) one or more detection markers selected from the group consisting of intestinal

short chain fatty acids, wherein the intestinal short chain fatty acid is propionate, in an

experimental animal untreated with the test substance and the experimental animal

administered with the test substance; and

(3) comparing the measured results in a control group untreated with the test substance

and the experimental animal administered with the test substance.
[Claim 15]

The method according to claim 14, wherein the (a) microbial biomarkers of

nonalcoholic fatty liver disease are one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceae, Ruminococcaceae.
[Claim 16]

The method according to claim 14, wherein the (a) microbial biomarkers of

nonalcoholic fatty liver disease are one or more selected from the group consisting of

Citrobacter,Megamonas, Ruminococcus, Faecalibacterium,Coprococcus and Lachnospira.
[Claim 17]

The method for screening a therapeutic agent of nonalcoholic fatty liver according to claim 14, wherein the method comprises one or more steps selected from combinations of (a) to (p) below:

(a) comparing abundances of one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceaeand Ruminococcaceae, measured before and after

administration of a candidate substance of therapeutic agent;

(b) comparing the contents in feces of one or more selected from the group consisting

of total bile acid, cholic acid, chenodeoxycholic acid and ursodeoxycholic acid, measured

before and after administration of a candidate substance of therapeutic agent;

(c) comparing the contents in feces of propionate, measured before and after

administration of a candidate substance of therapeutic agent;

(d) comparing abundances or the contents in feces of one or more selected from the

group consisting of Enterobacteriaceae, Veillonellaceae,Ruminococcaceae, cholic acid,

chenodeoxycholic acid, ursodeoxycholic acid and propionate, measured before and after

administration of a candidate substance of therapeutic agent;

(e) comparing abundances or the contents in feces of one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira, cholic acid,

chenodeoxycholic acid and ursodeoxycholic acid, measured before and after administration of

a candidate substance of therapeutic agent;

(f) comparing abundances or the contents in feces of one or more selected from the

group consisting of Ruminococcus, Faecalibacterium, Coprococcus, Lachnospira and feces

propionate, measured before and after administration of a candidate substance of therapeutic

agent;

(g) comparing abundances or the contents in feces of one or more selected from the

group consisting of Megamonas, cholic acid, chenodeoxycholic acid and ursodeoxycholic acid,

measured before and after administration of a candidate substance of therapeutic agent;

(h) comparing abundances or the contents in feces of one or more selected from the

group consisting of Megamonas and feces propionate, measured before and after

administration of a candidate substance of therapeutic agent;

(i) comparing abundances of one or more selected from the group consisting of

Enterobacteriaceae, Veillonellaceae, Ruminococcaceae, Citrobacter, Megamonas,

Ruminococcus and Faecalibacterium, measured before and after administration of a candidate

substance of therapeutic agent;

(j) comparing the contents in feces of one or more selected from the group consisting

of cholic acid, chenodeoxycholic acid, ursodeoxycholic acid and metabolites thereof, measured

before and after administration of a candidate substance of therapeutic agent;

(k) comparing the contents in feces of propionate, measured before and after

administration of a candidate substance of therapeutic agent;

(1) comparing abundances of Enterobacteriaceae, Veillonellaceae, and

Ruminococcaceae, measured before and after administration of a candidate substance of

therapeutic agent;

(m) comparing abundances of Megamonas and Ruminococcus, measured before and

after administration of a candidate substance of therapeutic agent;

(n) comparing the contents in feces of cholic acid, chenodeoxycholic acid,

ursodeoxycholic acid and propionate, measured before and after administration of a candidate

substance of therapeutic agent;

(o) comprising the (1) and (n); and

(p) comprising the (m) and (n).